103 research outputs found
Industry-scale application and evaluation of deep learning for drug target prediction
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic functions
to build a parameterization of conditional cumulative distribution functions
(CDFs). By the application of automatic differentiation with respect to
response variables and then to parameters of this CDF representation, we are
able to build black box CDF and density estimators. A suite of families is
introduced as alternative constructions for the multivariate case. At one
extreme, the simplest construction is a competitive density estimator against
state-of-the-art deep learning methods, although it does not provide an easily
computable representation of multivariate CDFs. At the other extreme, we have a
flexible construction from which multivariate CDF evaluations and
marginalizations can be obtained by a simple forward pass in a deep neural net,
but where the computation of the likelihood scales exponentially with
dimensionality. Alternatives in between the extremes are discussed. We evaluate
the different representations empirically on a variety of tasks involving tail
area probabilities, tail dependence and (partial) density estimation.Comment: 10 page
Tensor-train kernel learning for Gaussian processes
We propose a new kernel learning approach based on efficient low-rank tensor compression for Gaussian process (GP) regression. The central idea is to compose a low-rank function represented in a hierarchical tensor format with a GP covariance function. Compared to similar deep neural network architectures, this approach facilitates to learn significantly more expressive features at lower computational costs as illustrated in the examples. Additionally, over-fitting is avoided with this compositional model by taking advantage of its inherent regularisation properties. Estimates of the generalisation error are compared to five baseline models on three synthetic and six real-world data sets. The experimental results show that the incorporated tensor network enables a highly accurate GP regression with a comparatively low number of trainable parameters. The observed performance is clearly superior (usually by an order of magnitude in mean squared error) to all examined standard models, in particular to deep neural networks with more than 1000 times as many parameters
Robust Visual Imitation Learning with Inverse Dynamics Representations
Imitation learning (IL) has achieved considerable success in solving complex
sequential decision-making problems. However, current IL methods mainly assume
that the environment for learning policies is the same as the environment for
collecting expert datasets. Therefore, these methods may fail to work when
there are slight differences between the learning and expert environments,
especially for challenging problems with high-dimensional image observations.
However, in real-world scenarios, it is rare to have the chance to collect
expert trajectories precisely in the target learning environment. To address
this challenge, we propose a novel robust imitation learning approach, where we
develop an inverse dynamics state representation learning objective to align
the expert environment and the learning environment. With the abstract state
representation, we design an effective reward function, which thoroughly
measures the similarity between behavior data and expert data not only
element-wise, but also from the trajectory level. We conduct extensive
experiments to evaluate the proposed approach under various visual
perturbations and in diverse visual control tasks. Our approach can achieve a
near-expert performance in most environments, and significantly outperforms the
state-of-the-art visual IL methods and robust IL methods
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